Frederic Sampedro

Relationship between cortical thickness and cerebrospinal fluid YKL-40 in predementia stages of Alzheimer's disease

Cerebrospinal fluid YKL-40 has been described as a marker of glial inflammation. We aimed to study the relationship between YKL-40 and brain structure and its interactions with core Alzheimers disease (AD) biomarkers. We measured cortical thickness (CTh) and cerebrospinal fluid biomarkers (amyloid-β 1-42 [Aβ42], total tau, p-tau, and YKL-40) of 80 cognitively normal controls and 27 patients with amnestic mild cognitive impairment. Subjects were classified as Aβ42+ (<550 pg/mL) or Aβ42- (>550 pg/mL). CTh difference maps were derived from the interaction and correlation analyses in the whole sample and within clinical groups. There was a strong correlation between YKL-40 and markers of neurodegeneration (total tau and p-tau). In the whole sample, we found a negative correlation between YKL-40 and CTh in AD vulnerable areas in Aβ42+ subjects but not in Aβ42 participants. Our results suggest that YKL-40 could track the inflammatory processes associated to tau-related neurodegeneration in the presence of the AD pathophysiological process.

Neuropsychiatric symptoms are very common in premanifest and early stage Huntington's Disease.

BACKGROUND Neuropsychiatric symptoms are common features of Huntingtons disease (HD). Whereas most studies have focused on cognitive and neuroimaging markers of disease progression, little is known about the prevalence of neuropsychiatric symptoms in premanifest mutation carriers far-from and close-to disease onset. METHODS We obtained neurological, cognitive and behavioral data from 230 participants classified as premanifest far-from (preHD-A) and close-to (preHD-B) motor-based disease onset, early-symptomatic (early-HD), and healthy controls. Frequency and severity of neuropsychiatric symptoms were assessed with the short Problem Behaviors Assessment for HD (PBA-s). The odds-ratio (OR) to present symptoms in the clinical range was calculated using the control group as reference. Logistic regression analysis was used to explore relationships between neuropsychiatric symptoms and medication use. RESULTS Prevalence of depression was similar in all groups. Apathy was already present in 32% of preHD-A increasing to 62% of early-HD patients. The probability of presenting apathetic symptoms was 15-88 times higher in preHD-A and preHD-B respectively than in healthy controls. Irritability and executive dysfunction were present in both preHD-B and early-HD. CONCLUSION Neuropsychiatric symptoms are highly prevalent in HD, already in the premanifest stage, with increasing prevalence of irritability, apathy and executive dysfunction closer to onset. Compared to controls, HD mutation carriers have the highest probability to develop apathy, with an increasing prevalence along disease stages. Our findings confirm the high prevalence of neuropsychiatric symptoms in HD, already many years before the onset of motor symptoms, with apathy as an early manifestation and core neuropsychiatric feature of the disease.

Telling true from false: cannabis users show increased susceptibility to false memories

Previous studies on the neurocognitive impact of cannabis use have found working and declarative memory deficits that tend to normalize with abstinence. An unexplored aspect of cognitive function in chronic cannabis users is the ability to distinguish between veridical and illusory memories, a crucial aspect of reality monitoring that relies on adequate memory function and cognitive control. Using functional magnetic resonance imaging, we show that abstinent cannabis users have an increased susceptibility to false memories, failing to identify lure stimuli as events that never occurred. In addition to impaired performance, cannabis users display reduced activation in areas associated with memory processing within the lateral and medial temporal lobe (MTL), and in parietal and frontal brain regions involved in attention and performance monitoring. Furthermore, cannabis consumption was inversely correlated with MTL activity, suggesting that the drug is especially detrimental to the episodic aspects of memory. These findings indicate that cannabis users have an increased susceptibility to memory distortions even when abstinent and drug-free, suggesting a long-lasting compromise of memory and cognitive control mechanisms involved in reality monitoring.

Cocaine addiction is associated with abnormal prefrontal function, increased striatal connectivity and sensitivity to monetary incentives, and decreased connectivity outside the human reward circuit.

Cocaine addiction has been associated with increased sensitivity of the human reward circuit to drug‐related stimuli. However, the capacity of non‐drug incentives to engage this network is poorly understood. Here, we characterized the functional sensitivity to monetary incentives and the structural integrity of the human reward circuit in abstinent cocaine‐dependent (CD) patients and their matched controls. We assessed the BOLD response to monetary gains and losses in 30 CD patients and 30 healthy controls performing a lottery task in a magnetic resonance imaging scanner. We measured brain gray matter volume (GMV) using voxel‐based morphometry and white matter microstructure using voxel‐based fractional anisotropy (FA). Functional data showed that, after monetary incentives, CD patients exhibited higher activation in the ventral striatum than controls. Furthermore, we observed an inverted BOLD response pattern in the prefrontal cortex, with activity being highest after unexpected high gains and lowest after losses. Patients showed increased GMV in the caudate and the orbitofrontal cortex, increased white matter FA in the orbito‐striatal pathway but decreased FA in antero‐posterior association bundles. Abnormal activation in the prefrontal cortex correlated with GMV and FA increases in the orbitofrontal cortex. While functional abnormalities in the ventral striatum were inversely correlated with abstinence duration, structural alterations were not. In conclusion, results suggest abnormal incentive processing in CD patients with high salience for rewards and punishments in subcortical structures but diminished prefrontal control after adverse outcomes. They further suggest that hypertrophy and hyper‐connectivity within the reward circuit, to the expense of connectivity outside this network, characterize cocaine addiction.

Longitudinal brain structural changes in preclinical Alzheimer's disease.

Brain structural changes in preclinical Alzheimers disease (AD) are poorly understood.

Assessing the psychedelic "after-glow" in ayahuasca users: post-acute neurometabolic and functional connectivity changes are associated with enhanced mindfulness capacities

Abstract Background Ayahuasca is a plant tea containing the psychedelic 5-HT2A agonist N,N-dimethyltryptamine and harmala monoamine-oxidase inhibitors. Acute administration leads to neurophysiological modifications in brain regions of the default mode network, purportedly through a glutamatergic mechanism. Post-acutely, ayahuasca potentiates mindfulness capacities in volunteers and induces rapid and sustained antidepressant effects in treatment-resistant patients. However, the mechanisms underlying these fast and maintained effects are poorly understood. Here, we investigated in an open-label uncontrolled study in 16 healthy volunteers ayahuasca-induced post-acute neurometabolic and connectivity modifications and their association with mindfulness measures. Methods Using 1H-magnetic resonance spectroscopy and functional connectivity, we compared baseline and post-acute neurometabolites and seed-to-voxel connectivity in the posterior and anterior cingulate cortex after a single ayahuasca dose. Results Magnetic resonance spectroscopy showed post-acute reductions in glutamate+glutamine, creatine, and N-acetylaspartate+N-acetylaspartylglutamate in the posterior cingulate cortex. Connectivity was increased between the posterior cingulate cortex and the anterior cingulate cortex, and between the anterior cingulate cortex and limbic structures in the right medial temporal lobe. Glutamate+glutamine reductions correlated with increases in the “nonjudging” subscale of the Five Facets Mindfulness Questionnaire. Increased anterior cingulate cortex-medial temporal lobe connectivity correlated with increased scores on the self-compassion questionnaire. Post-acute neural changes predicted sustained elevations in nonjudging 2 months later. Conclusions These results support the involvement of glutamate neurotransmission in the effects of psychedelics in humans. They further suggest that neurometabolic changes in the posterior cingulate cortex, a key region within the default mode network, and increased connectivity between the anterior cingulate cortex and medial temporal lobe structures involved in emotion and memory potentially underlie the post-acute psychological effects of ayahuasca.

Non-demented Parkinson’s disease patients with apathy show decreased grey matter volume in key executive and reward-related nodes

Apathy is a common but poorly understood neuropsychiatric disturbance in Parkinson’s disease (PD). In a recent study using event-related brain potentials we demonstrated impaired reward processing and compromised mesocortico-limbic pathways in PD patients with clinical symptoms of apathy. Here we aimed to further investigate the involvement of reward circuits in apathetic PD patients by assessing potential differences in brain structure. Using structural magnetic resonance imaging (MRI) and voxel-based morphometry (VBM) we quantified grey matter volume (GMV) in a sample of 18 non-demented and non-depressed PD patients with apathy, and 18 matched non-apathetic patients. Both groups were equivalent in terms of sociodemographic characteristics, disease stage, cognitive performance and L-Dopa equivalent daily dose. Apathetic patients showed significant GMV loss in cortical and subcortical brain structures. Various clusters of cortical GMV decrease were found in the parietal, lateral prefrontal cortex, and orbitofrontal cortex (OFC). The second largest cluster of GMV loss was located in the left nucleus accumbens (NAcc), a subcortical structure that is a key node of the human reward circuit. Isolated apathy in our sample is explained by the combined GMV loss in regions involved in executive functions, and cortical and subcortical structures of the mesolimbic reward pathway. The correlations observed between apathy and cognition suggests apathy as a marker of more widespread brain degeneration even in a sample of non-demented PD patients.

Iterative multi-class multi-scale stacked sequential learning: Definition and application to medical volume segmentation

In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios.

Obtaining quantitative global tumoral state indicators based on whole-body PET/CT scans: a breast cancer case study.

ObjectivesIn this work we address the need for the computation of quantitative global tumoral state indicators from oncological whole-body PET/computed tomography scans. The combination of such indicators with other oncological information such as tumor markers or biopsy results would prove useful in oncological decision-making scenarios. Materials and methodsFrom an ordering of 100 breast cancer patients on the basis of oncological state through visual analysis by a consensus of nuclear medicine specialists, a set of numerical indicators computed from image analysis of the PET/computed tomography scan is presented, which attempts to summarize a patient’s oncological state in a quantitative manner taking into consideration the total tumor volume, aggressiveness, and spread. ResultsResults obtained by comparative analysis of the proposed indicators with respect to the experts’ evaluation show up to 87% Pearson’s correlation coefficient when providing expert-guided PET metabolic tumor volume segmentation and 64% correlation when using completely automatic image analysis techniques. ConclusionGlobal quantitative tumor information obtained by whole-body PET/CT image analysis can prove useful in clinical nuclear medicine settings and oncological decision-making scenarios. The completely automatic computation of such indicators would improve its impact as time efficiency and specialist independence would be achieved.

A computational framework for cancer response assessment based on oncological PET-CT scans

In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.